A multi-objective, coordinated optimization method, equipment, medium, and product based on water conservation, pollution reduction, carbon reduction, greening, and growth.

Abstract

This application discloses a multi-objective coordinated optimization method, equipment, medium, and product based on water conservation, pollution reduction, carbon reduction, greening, and growth, relating to the field of regional ecological protection. The method includes: calculating water consumption, pollutant emissions, carbon dioxide emissions, economic benefits, and ecosystem service value for various industries under multiple dimensions using an accounting model; obtaining the model parameters required for the optimization model; determining decision variables, objective functions, and constraints; and constructing a multi-objective optimization model to achieve water conservation, pollution reduction, carbon reduction, greening, and growth. A third-generation non-dominated sorting genetic algorithm is used to iteratively calculate the multi-objective optimization model, generating a Pareto optimal solution set. A TOPSIS method based on combined weighting is used for comprehensive evaluation and ranking, outputting the optimal configuration scheme with the highest comprehensive score. This application accurately characterizes the trade-offs and synergistic relationships between various objectives and efficiently identifies the optimal solution set that takes into account multiple dimensions.

Description

Technical Field

[0001] This application relates to the field of watershed ecological protection, and in particular to a multi-objective integrated and coordinated optimization method, equipment, medium and product based on water conservation, pollution reduction, carbon reduction, greening expansion and growth. Background Technology

[0002] The multi-objective synergistic regulation of water conservation, pollution reduction, carbon reduction, greening, and growth has become a core issue in regional resource and environmental management. Current research on this synergistic effect largely focuses on theoretical discussions or the construction of evaluation index systems, typically employing methods such as coupling coordination degree models and obstacle degree models to quantitatively analyze and evaluate the synergistic level of regions or cities. However, such evaluation methods only reflect the current level of development and static correlations, lacking quantitative path optimization analysis oriented towards multi-objective synergy, and are insufficient to support dynamic regulation decisions in complex systems. Furthermore, existing optimization modeling research often focuses on a single industry or a single objective (such as focusing only on the synergy of pollution reduction and carbon reduction), failing to comprehensively incorporate key industries such as industry, agriculture, fisheries, and animal husbandry into the optimization system within a unified analytical framework, and lacking a systematic overall coordination of the five dimensions of "water conservation, pollution reduction, carbon reduction, greening, and growth." Under multiple resource and environmental constraints and policy planning restrictions, existing technologies are unable to accurately depict the trade-offs and synergies between various objectives, and cannot efficiently identify the optimal solution set that takes into account multiple dimensions of demands. This leads to actual management relying heavily on experience-based judgment or "one-size-fits-all" control, which restricts the synergistic improvement of the green and low-carbon transformation of industrial structure and resource and environmental benefits. Summary of the Invention

[0003] The purpose of this application is to provide a multi-objective coordinated optimization method, equipment, medium and product based on water conservation, pollution reduction, carbon reduction, greening and growth, in order to solve the problem that it is difficult to accurately characterize the trade-offs and synergistic relationships between the objectives and to efficiently identify the optimal solution set that takes into account multiple dimensions.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a multi-objective, coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, including: The calculation model is used to calculate the water consumption, pollutant emissions, carbon dioxide emissions, economic benefits and ecological service value of various industries under multiple dimensions, and obtain the model parameters required for the optimization model. Based on the model parameters, decision variables, objective functions, and constraints are determined, and a multi-objective optimization model is constructed to achieve water conservation, pollution reduction, carbon reduction, greening, and growth. The objective functions include minimizing water consumption, minimizing chemical oxygen demand (COD) emissions, minimizing ammonia nitrogen emissions, maximizing economic benefits, minimizing carbon dioxide emissions, maximizing ecosystem service value, minimizing total phosphorus emissions, and minimizing total nitrogen emissions. The objective function for minimizing water consumption is as follows: min f 1 represents minimizing water consumption; M This is the sub-region index; m is the sub-region number; I Index for industrial sector types; i For industrial sector type serial numbers; J Crop type index; j For crop type serial number; Y Index for aquaculture types; y This refers to the serial number of the aquaculture type; K Index for livestock type; k For livestock type serial number; wIN mi Let i be the water consumption coefficient for industry i in subregion m; IN mi Let i be the industrial output value of industry i in subregion m; WC mj Let be the water use coefficient of crop j in subregion m; C mj Let be the planting area of ​​crop j in subregion m; WEF my The water consumption coefficient of type y in subregion m; EF my Let m be the aquaculture area of ​​type y in subregion m; S mk The proportion of livestock raised by farmers in subregion m is the proportion of livestock raised by farmers raising k animals. WS mk Let be the water consumption coefficient for livestock farmers with k animals in subregion m; FS mk Let m be the proportion of livestock raised by scale in subregion m; WFS mk Let be the water consumption coefficient for livestock farming of size k in subregion m; LS mk Let m be the average annual number of livestock (k) in subregion m. The objective function that maximizes the value of ecosystem services is: ; maxf 6 To maximize the value of ecosystem services; GC mj Ecosystem service value coefficient of crop j in subregion m; GEF my represents the ecosystem service value coefficient of type y in subregion m; The objective function for minimizing total phosphorus emissions is: ; minf 7 To minimize total phosphorus emissions; PIN mi Let be the TP emission coefficient of industry i in subregion m; PC mj Let be the TP emission coefficient of crop j in subregion m; PS mk Let be the TP emission coefficient for livestock farmers with k livestock in subregion m; PFS mk Let be the TP emission coefficient for livestock farming of size k in subregion m; PEF my The TP emission coefficient of type y in subregion m; The objective function for minimizing total nitrogen emissions is: ; minf 8 To minimize total nitrogen emissions; DIN mi Let be the TP emission coefficient of industry i in subregion m; DC mj Let be the TP emission coefficient of crop j in subregion m; DS mk Let be the TP emission coefficient for livestock farmers with k livestock in subregion m; DFS mk Let be the TP emission coefficient for livestock farming of size k in subregion m; DEF my The TP emission coefficient of type y in subregion m; A third-generation non-dominated sorting genetic algorithm is used to iteratively calculate a multi-objective optimization model containing the aforementioned decision variables, objective function, and constraints to generate a Pareto optimal solution set. Based on the Pareto optimal solution set, the TOPSIS method based on combined weighting is used for comprehensive evaluation and ranking, and the optimal configuration scheme with the highest comprehensive score is output; the optimal configuration scheme is a water-saving, pollution-reducing, carbon-reducing, greening and growth synergistic optimization scheme.

[0005] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth.

[0006] Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-mentioned multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth.

[0007] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application constructs model parameters for an optimization model covering water consumption, pollutant emissions, carbon dioxide emissions, economic benefits, and ecosystem service value across various industries in multiple dimensions. This transforms traditional static evaluation into quantitative dynamic path optimization, effectively addressing the problem that existing technologies can only assess the current situation but lack quantitative control support. Based on the model parameters, decision variables, objective functions, and constraints are determined, constructing a multi-objective optimization model to achieve water conservation, pollution reduction, carbon reduction, greening, and growth. This model integrates multiple industries into a unified "water conservation-pollution reduction-carbon reduction-greening-growth" system, setting constraints to accurately characterize the complex trade-offs and synergistic relationships among multi-dimensional objectives, overcoming the limitations of single-industry or single-objective optimization. Furthermore, a third-generation non-dominated sorting genetic algorithm is used to efficiently iteratively solve the high-dimensional multi-objective model, generating a Pareto optimal solution set that satisfies multiple constraints. This is combined with subjective and objective weighting and the "Technique for Order Preference by Similarity to an Ideal Solution" method. The TOPSIS (Top-Optimal Solution) method scientifically sorts and selects the best solution from a massive number of non-dominated solutions, enabling the rapid and objective selection of the optimal configuration with the highest comprehensive score. This application can accurately characterize the trade-offs and synergistic relationships between various objectives, efficiently identify the optimal solution set that takes into account multiple dimensions, and completely abandon the traditional experience-based decision-making and "one-size-fits-all" management model. It provides high-precision, interpretable, and highly practical quantitative technical support for the green and low-carbon transformation of the basin's industrial structure, the refined management of resources and environment, and multi-objective collaborative decision-making. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the solution process of a multi-objective optimization model provided in an embodiment of this application; Figure 3 A schematic diagram of the Pareto solution set and TOPSIS comprehensive score results obtained from multi-objective optimization of an economic zone, as provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the optimization results of industrial output value of various provinces and cities in an economic zone, provided as an embodiment of this application; Figure 5 This is a schematic diagram illustrating the optimized crop planting area results for various provinces and cities in an economic zone, as provided in an embodiment of this application. Figure 6 This is a schematic diagram illustrating the optimization results of livestock breeding volume in various provinces and cities of a certain economic zone, provided as an embodiment of this application. Figure 7 This is a schematic diagram illustrating the optimization results of aquaculture area in various provinces and cities of a certain economic zone, provided as an embodiment of this application. Figure 8 A schematic diagram illustrating the optimized water consumption results of various provinces and cities in an economic zone, provided as an embodiment of this application; Figure 9 A schematic diagram illustrating the optimized emissions of major pollutants (NH3-N, COD, TN, TP) in various provinces and cities of an economic zone, provided as an embodiment of this application; Figure 10 A schematic diagram illustrating the optimization results of carbon emissions in various provinces and cities of a certain economic zone, provided as an embodiment of this application; Figure 11 A schematic diagram illustrating the optimization results of the ecosystem service value of various provinces and cities in an economic belt, provided as an embodiment of this application; Figure 12 This is a schematic diagram illustrating the economic benefit optimization results of various provinces and cities provided in an embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1 As shown, this application provides a multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, including: S1: Calculate water consumption, pollutant emissions, carbon dioxide emissions, economic benefits, and ecosystem service value of various industries under multiple dimensions through the accounting model to obtain the model parameters required for optimization.

[0014] S2: Based on the model parameters, determine the decision variables, objective functions, and constraints, and construct a multi-objective optimization model to achieve water conservation, pollution reduction, carbon reduction, greening, and growth; the objective functions include minimizing water consumption, minimizing chemical oxygen demand emissions, minimizing ammonia nitrogen emissions, maximizing economic benefits, minimizing carbon dioxide emissions, maximizing ecosystem service value, minimizing total phosphorus emissions, and minimizing total nitrogen emissions.

[0015] S3: The third-generation non-dominated sorting genetic algorithm is used to iteratively calculate the multi-objective optimization model containing the decision variables, objective function and constraints to generate the Pareto optimal solution set.

[0016] S4: Based on the Pareto optimal solution set, the TOPSIS method based on combined weighting is used for comprehensive evaluation and ranking, and the optimal configuration scheme with the highest comprehensive score is output; the optimal configuration scheme is a water-saving, pollution-reducing, carbon-reducing, greening and growth synergistic optimization scheme.

[0017] In an exemplary embodiment, S1 specifically includes: S11: Calculate industrial water consumption based on industrial output value and industrial water intensity, and calculate agricultural water consumption based on planting area, average annual livestock breeding volume and aquaculture area and corresponding water consumption coefficients to obtain water consumption coefficients for each industry in each sub-region. S12: The carbon emission coefficient method is used to calculate the carbon dioxide emissions of industry, planting, animal husbandry and fishery respectively; among them, the carbon emission calculation of planting and fishery includes the direct carbon emissions from material input and energy consumption, indirect carbon emissions of greenhouse gases and carbon absorption, to obtain the carbon dioxide emission coefficient of each industry in each sub-region. S13: Calculate the total emissions of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen based on the unit emission coefficients of each industry, and obtain the pollutant emission coefficients of each industry in each sub-region; S14: Calculate economic benefits based on historical output data, and calculate ecological service value based on standard ecological service value equivalent factor to obtain economic benefit coefficients and ecological service value coefficients for each industry in each sub-region. S15: Substitute the water consumption coefficient, carbon dioxide emission coefficient, pollutant emission coefficient, economic benefit coefficient, and ecosystem service value coefficient of each industry in each sub-region into the multi-objective optimization model as the model parameters, and use the total amount of each indicator in the base year as the upper or lower limit of the constraints.

[0018] In practical applications, S1 is the parameter preparation step for a multi-objective optimization model, which specifically includes: I. Water consumption calculation.

[0019] (1) Industrial water consumption: (1) In the formula, i Industrial type; WIN This refers to industrial water consumption. IN Industrial output value; Industrial water intensity.

[0020] (2) Agricultural water consumption: Water use for agriculture (including crop farming, animal husbandry, and fisheries) shall be calculated with reference to the "Agricultural Water Use Quotas" of each province and city.

[0021] (2) In the formula, j, k, y These are respectively crop, livestock, and aquaculture types; WA For agricultural water consumption; C For crop planting area; LS This refers to the average annual number of livestock raised. EF Area for aquaculture; WC , WLS , WEF These are the water use coefficients for agriculture, animal husbandry, and fisheries, respectively.

[0022] II. Calculation of carbon dioxide emissions.

[0023] (1) Industrial carbon dioxide emissions: The industrial sector carbon emission data comes from the China Emission Accounts and Datasets (CEADs) database developed by Guan Dabao's team.

[0024] (2) Net carbon dioxide emissions from crop farming: Currently, there is a lack of direct and effective technical means for measuring and monitoring carbon emissions from crop farming, and a unified accounting method has not yet been established in academic circles both in China and abroad. This application calculates carbon emissions from crop farming based on the carbon emission factor method published by the Intergovernmental Panel on Climate Change (IPCC). The accounting process mainly considers the carbon dioxide generated by agricultural inputs, while also taking into account the carbon dioxide absorbed by straw return to the field and crop roots. The specific calculation formula is as follows: (3) Direct carbon emissions from agricultural inputs: (4) (5) In the formula, CE fer-N For nitrogen fertilizer carbon emissions, α fer-N This refers to the amount of nitrogen fertilizer applied per unit area. q fer-NThis represents the carbon emission coefficient for nitrogen fertilizer.

[0025] (6) In the formula, CE fer-p Carbon emissions from phosphate fertilizers α fer-p This refers to the amount of phosphate fertilizer applied per unit area. q fer-p This represents the carbon emission coefficient for phosphate fertilizer.

[0026] (7) In the formula, CE fer-k Carbon emissions from potash fertilizer α fer-k This refers to the amount of potassium fertilizer applied per unit area. q fer-k This represents the carbon emission coefficient for potash fertilizer.

[0027] (8) In the formula, CE fer-com For carbon emissions from compound fertilizers, α fer-com This refers to the amount of compound fertilizer applied per unit area. q fer-com This represents the carbon emission coefficient of compound fertilizer.

[0028] (9) In the formula, CE pes Carbon emissions from pesticides α pes This refers to the amount of pesticide applied per unit area. q pes This represents the carbon emission coefficient for pesticides.

[0029] (10) In the formula, CE film For carbon emissions from agricultural film, α film This refers to the amount of agricultural film applied per unit area. q film This represents the carbon emission coefficient of agricultural film.

[0030] (11) In the formula, CE mach Carbon emissions from agricultural machinery α mach The power of agricultural machinery per unit area q mach This represents the carbon emission coefficient for agricultural machinery.

[0031] (12) In the formula, CE ele For carbon emissions from electricity, α ele Electricity consumption per unit of irrigation water q ele Carbon emission coefficient of irrigation electricity, wu, and water consumption per unit irrigation area.

[0032] Indirect carbon emissions from crops: (13) (14) In the formula, CE pad Indirect carbon emissions of CH4 from rice paddies β pad CH4 emissions per unit area of ​​paddy field.

[0033] (15) In the formula, CE fer Indirect carbon emissions from fertilizer N2O α fer-N Nitrogen fertilizer per unit area, α fer-com Compound fertilizer per unit area ω c Nitrogen mass fraction of compound fertilizer D SN The input soil nitrogen fertilizer N2O direct emission coefficient, 44 / 28 is the N to N2O conversion coefficient; 298 is the 100-year global warming potential of N2O.

[0034] (16) In the formula, CE org For organic carbon loss N2O emissions, ρ b Unit weight of arable soil, D soil surface layer thickness, ω SOM Mass fraction of soil organic carbon, ϖ average annual reduction rate of soil organic carbon, R CN Soil organic matter carbon-nitrogen ratio, D SON N2O emission coefficient caused by organic carbon loss.

[0035] (17) In the formula, CO2 emissions from straw burning; η j The ratio of straw to grain in crops; hCombustion efficiency; δ represents the proportion of open-air burning; λ j Let be the greenhouse gas emissions from burning the straw of the j-th crop.

[0036] Carbon dioxide absorption is calculated using the following formula: (18) (19) In the formula, AB straw Y represents the CO2 absorption rate of straw returned to the field. j Let be the yield per unit area of ​​crop j; η j The ratio of straw to grain in crops; a jst Straw return rate; μ , This is an empirical coefficient.

[0037] (20) In the formula, AB root This refers to the amount of CO2 absorbed by crop roots. b st Crown-to-root ratio; v This refers to the carbon storage in the root system.

[0038] Net carbon emissions are: (twenty one) (3) Carbon dioxide emissions from animal husbandry: Different livestock and poultry breeds have different breeding cycles, so their average annual feeding volume needs to be adjusted accordingly. The specific adjustment method is shown in the formula. The livestock and poultry species involved in the adjustment mainly include pigs and poultry, whose breeding cycles are 200 days and 55 days, respectively.

[0039] (twenty two) In the formula, LS represents the average annual feeding quantity. Herds end This refers to the number of animals in stock at the end of the year. Days For the feeding cycle, Ave This refers to the annual number of pigs slaughtered.

[0040] Greenhouse gas emissions from China's livestock industry mainly originate from processes such as intestinal fermentation in livestock and poultry, manure treatment, and manure return to the fields, with CH4 and N2O being significant carbon sources. This application identifies dairy cows, beef cattle, pigs, sheep, and poultry as the primary carbon emission accounting targets. For ease of comparison, CH4 and N2O are converted into CO2 equivalents, with conversion factors of 1 t CH4 equivalent to 25 t CO2 and 1 t N2O equivalent to 298 t CO2. The constructed calculation formula is as follows: (twenty three) In the formula: E herd This represents the total carbon dioxide emissions from livestock farming. LS k Let b be the number of the kth type of livestock raised. k c k d k These are the CH4 emission coefficients from intestinal fermentation and the CH4 and N2O emission coefficients from manure treatment for the kth type of livestock, respectively.

[0041] (4) Carbon dioxide emissions from fisheries: This application focuses on carbon emission accounting for freshwater aquaculture models, classifying them into two types: extensive and semi-intensive aquaculture, covering ponds, lakes, reservoirs, and rivers; and rice-fish farming, specifically referring to the rice-fish co-culture model conducted in paddy fields. CO2 emissions from freshwater aquaculture can be categorized into direct and indirect emissions. Direct emissions mainly originate from agricultural inputs (such as feed, fertilizers, and organic fertilizers) and energy consumption (such as electricity used for water pumps and aeration equipment) during aquaculture activities. Indirect emissions include emissions from aquaculture sediment, wastewater treatment, and CH4 and N2O released during rice growth in the rice-fish co-culture model. The specific accounting formula is as follows: (twenty four) In the formula, Q all The total carbon emissions for different aquaculture models; Q dir For direct carbon emissions; Q in This refers to indirect carbon emissions.

[0042] Direct carbon emissions from aquaculture: (25) In the formula: P y E represents the CO2 emissions from the input of aquaculture production materials of type y; y This represents the CO2 emissions from energy inputs in the y-th type of aquaculture.

[0043] Carbon emissions from production inputs primarily originate from CO2 released during the use of feed, chemical fertilizers, and organic fertilizers. Feed consumption is estimated based on feed conversion ratio and aquaculture yield, with the feed conversion ratio varying depending on the specific aquaculture model. The use of chemical fertilizers and organic fertilizers is only applicable to rice-fish farming models, mainly for rice production. The application rate is calculated based on the average application rate per unit area and the rice-fish farming area. The specific calculation formula is as follows: (26) In the formula: PE1 represents the CO2 emissions from feed input; PE2 represents the CO2 emissions from fertilizer input; PE3 represents the CO2 emissions from organic fertilizer input.

[0044] Carbon emissions from feed inputs: (27) Carbon emissions from fertilizer input: (28) Carbon emissions from organic fertilizer input: (29) Carbon emissions from energy inputs primarily originate from CO2 released during electricity and fuel consumption. For extensive and semi-intensive aquaculture models, the electricity demand of equipment such as water pumps and aerators is mainly considered. For rice-fish farming models, the fuel consumption of gasoline-powered equipment is primarily considered. Various energy consumption parameters are calculated based on energy intensity per unit area or per unit yield, combined with the area or yield of different aquaculture models. The specific calculation formulas are as follows: (30) In the formula, EN1 represents CO2 emissions from electricity consumption; EN2 represents CO2 emissions from fuel consumption.

[0045] Carbon emissions from electricity consumption: (31) Carbon emissions from fuel consumption: (32) Indirect carbon emissions from aquaculture mainly consist of two greenhouse gases: CH4 and N2O. The emissions of CH4 and N2O under different aquaculture models are calculated based on the emission coefficient per unit aquaculture area and the actual aquaculture area. The specific calculation formula is as follows: (33) In the formula, M y The greenhouse gas emissions of CH4 from type y aquaculture during the aquaculture process; N y Let N2O be the greenhouse gas emissions from the yth type of aquaculture.

[0046] CH4 carbon emissions from aquaculture: (34) N2O carbon emissions from aquaculture: (35) III. Calculation of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen emissions.

[0047] Emissions from agricultural sources (including crop farming, animal husbandry, and fisheries) are calculated with reference to the "Handbook of Production and Emission Coefficients for Agricultural Pollution Sources". Animal husbandry emissions consider both large-scale and small-scale farming, with the proportion of large-scale farming referencing the animal husbandry development plans of each province and city. Industrial sources utilize historical COD, NH3-N, and TP emission data from each province to calculate COD, NH3-N, and TP emission coefficients.

[0048] (36) In the formula, TCOD Total COD emissions; OIN , OC , OS , OFS , OEF These are the unit COD emissions from industrial enterprises, planting industries, livestock farming households, large-scale livestock farming, and fisheries, respectively.

[0049] (37) In the formula, TNH 3 -N for NH 3 -N Total emissions, in tons; NIN , NC , NS , NFS , NEF These represent the unit NH3-N emissions from industrial enterprises, planting industries, livestock farming households, large-scale livestock farming, and fisheries, respectively.

[0050] (38) In the formula, TP Total phosphorus emissions, in tons (t). PIN , PC , PS , PFS , PEF These are the unit TP emissions for industrial enterprises, planting industries, livestock farming households, large-scale livestock farming, and fisheries, respectively.

[0051] (39) In the formula, TN Total nitrogen emissions, in tons (t). DIN , DC , DS , DFS , DEF These are the unit TN emissions for industrial enterprises, planting industries, livestock farming households, large-scale livestock farming, and fisheries, respectively.

[0052] IV. Economic benefits.

[0053] The calculation of agricultural output value (including crop farming, animal husbandry, and fisheries) is based on references to the "Compilation of National Agricultural Product Cost and Benefit Data" and the "China Fisheries Statistical Yearbook".

[0054] (40) In the formula, ECO Total output value, in RMB 100 million; EC , ES EFS EEF These are the unit output values ​​for planting, livestock farming by individual households, large-scale livestock farming, and fisheries, respectively.

[0055] V. Calculation of Ecosystem Service Value.

[0056] This application selects ecosystem service value as a key indicator to characterize the "greening effect". Referring to the research method of Zhang Lijin et al., the economic value of a standard ecosystem service value equivalent factor is set at 1 / 7 of the market value of the national average grain yield per unit area in that year. The value calculation of grain yield in farmland ecosystems is based on crops such as rice, corn, peanuts, rapeseed, and wheat.

[0057] (41) In the formula, Ecosystem service value (yuan / ha) for one standard equivalent factor; Let j be the yield of crop j; Let be the unit price of crop j; The total sown area of ​​different crops; 1 / 7 means that under natural conditions without human intervention, the economic value provided by the ecosystem is equivalent to one-seventh of the economic value of food production services provided by the ecosystem of the same area of ​​farmland.

[0058] Referring to China's ecosystem service value equivalent system per unit area, the regional ecosystem service value is calculated using the following specific formula: (42) In the formula, ESV represents the ecosystem service value (in yuan); A j D represents the area (ha) of the j-th land type. ij The value equivalent of serving the i-th ecosystem for the j-th land type.

[0059] In one exemplary embodiment, the decision variables include the industrial output of each industrial sector in the sub-region, the planting area of ​​various crops, the average annual number of livestock raised, and the aquaculture area of ​​various types of fisheries.

[0060] The objective functions are constructed as linear combinations of decision variables and corresponding model parameters, respectively minimizing water consumption, minimizing chemical oxygen demand emissions, minimizing ammonia nitrogen emissions, maximizing economic benefits, minimizing carbon dioxide emissions, maximizing ecosystem service value, minimizing total phosphorus emissions, and minimizing total nitrogen emissions. Each objective function participates in fitness assessment simultaneously during the iterative calculation process.

[0061] In one exemplary embodiment, the constraints include economic development constraints, scale expansion restrictions, pollutant emission restrictions, livestock development planning restrictions, fishery development planning constraints, agricultural development planning constraints, and water resource availability constraints.

[0062] The economic development constraint configuration is such that the optimized economic benefits are greater than the unoptimized economic benefits.

[0063] The scale expansion constraint is set based on the highest expansion rate in recent years and the lowest scale in the past five years to set the upper and lower limits of the decision variables.

[0064] The pollutant emission limit constraint is configured such that the optimized pollutant emission reduction rate is greater than the minimum reduction rate required by the policy plan.

[0065] The water resource availability constraint is configured such that the optimized total water consumption is lower than the upper limit of the total available water resources in the basin.

[0066] The multi-objective optimization model performs optimization calculations within the feasible region defined by the constraints.

[0067] In practical applications, S2 represents the steps for constructing and solving a multi-objective optimization model, specifically including: Construction of the objective function: (1) Minimal water consumption.

[0068] This goal aims to improve water resource utilization efficiency and minimize water consumption. It ensures the rational allocation of water resources by optimizing water usage across various industries.

[0069] (43) (2) The chemical oxygen demand (COD) emissions are the lowest.

[0070] This goal aims to reduce COD emissions, decrease water pollution, improve water resource efficiency, protect water quality, and safeguard the ecological environment of the watershed.

[0071] (44) (3) NH3-N emissions are the lowest.

[0072] This goal aims to reduce ammonia nitrogen emissions, decrease eutrophication, protect water quality, and enhance the utilization value of water resources within the basin.

[0073] (45) (4) It has the greatest economic benefits.

[0074] This goal aims to optimize the layout and resource allocation of various industries within the basin, improve economic efficiency, promote sustainable economic development within the basin, and maximize economic benefits.

[0075] (46) (5) The CO2 emissions are the lowest.

[0076] This goal aims to reduce carbon emissions from various industries within the basin, decrease greenhouse gas emissions, protect the atmospheric environment, address climate change, and safeguard the ecological environment within the basin.

[0077] (47) (6) Ecosystem services have the greatest value.

[0078] This goal aims to improve the overall function of the ecosystem, including water conservation, soil retention, climate regulation, and biodiversity protection, thereby safeguarding the ecological environment within the watershed.

[0079] (48) (7) The total phosphorus emissions are the lowest.

[0080] This goal aims to reduce total phosphorus emissions, protect the health of the aquatic ecosystem, improve water quality, and achieve sustainable use of water resources within the basin.

[0081] (49) (8) The total nitrogen emissions are the lowest.

[0082] This goal aims to reduce total nitrogen emissions, protect biodiversity, maintain ecological balance, and ensure drinking water safety, which is of great significance to ecosystem health and sustainable development.

[0083] (50) like Figure 2 As shown, this application constructs a total of 8 objective functions, which collectively correspond to the following constraints. These constraints not only apply to the objective functions but also to some variables. Furthermore, the constraints set here do not apply to all 8 objective functions. The meanings of the relevant symbols for the constraints are shown in Table 1.

[0084] Table 1

[0085] The constraints specifically include: (1) Economic development constraints.

[0086] This constraint aims to ensure that economic development and environmental protection within the basin are coordinated, and that the optimized economic benefits should be greater than the unoptimized economic benefits.

[0087] (51) (2) Restrictions on scale expansion.

[0088] This constraint aims to limit the development scale of various industries within the basin to avoid overexploitation and resource waste. It takes into account the largest expansion rate in recent years, setting limits on industrial scale expansion to prevent unrealistic expansion and the resource waste and environmental problems caused by overexpansion.

[0089] (52) (53) At the same time, the maximum and minimum scale values ​​over the years should be taken into account. To ensure supply, the optimized industrial scale should not be lower than the minimum value of the past five years.

[0090] (54) Note: The expansion rate is the annual growth rate of various industrial sectors.

[0091] (3) Pollutant emission restrictions.

[0092] This constraint aims to limit pollutant emissions from various industries within the watershed to reduce environmental pollution and ecological damage. According to relevant planning requirements, the optimized pollutant emission reduction rate should exceed the minimum reduction rate required by the plan.

[0093] (55) (56) (57) (58) (4) Restrictions and constraints of livestock development planning.

[0094] This constraint aims to ensure that the scale of livestock farming meets demand. According to relevant planning requirements, the optimized livestock development should meet those requirements.

[0095] (59) (5) Constraints of fisheries development planning.

[0096] These constraints aim to ensure the scale of fisheries development within the watershed, in order to protect aquatic biological resources and the ecological environment, while simultaneously meeting the demand for aquatic products. According to relevant planning requirements, the optimized fisheries development should meet those requirements.

[0097] (60) (61) (6) Constraints of agricultural development planning.

[0098] This constraint aims to ensure the needs of agricultural development within the watershed. According to relevant planning requirements, the optimized agricultural development should meet those requirements.

[0099] (62) (63) (7) Constraints on the availability of water resources.

[0100] This constraint sets an upper limit on the total regional water consumption, requiring that the optimized total water consumption must be lower than the total available water resources. It aims to systematically coordinate water use competition among various industries, thereby achieving intensive and economical use of water resources while ensuring the sustainability of water resources and the health of the watershed ecosystem.

[0101] (64) In an exemplary embodiment, S3 specifically includes: S31: Initialize the population, encode the decision variables as chromosome individuals, and set the population size and maximum number of evaluations.

[0102] S32: The third-generation non-dominated sorting genetic algorithm is adopted to perform non-dominated sorting mechanism to stratify the population individuals and calculate the crowding degree of individuals in combination with the reference point strategy.

[0103] S33: Based on the stratification results and individual crowding, perform selection, crossover, and mutation operations to generate offspring populations. After merging the parent and offspring, perform environmental selection. Repeat the iteration until the maximum number of evaluations is reached, and output a Pareto optimal solution set containing multiple sets of non-dominated decision variables. Each Pareto optimal solution satisfies the constraints and makes the objective function achieve multidimensional trade-off optimality.

[0104] In practical applications, the 3rd Generation Non-Dominant Sorting Genetic Algorithm (NSGA-Ⅲ), through its non-dominated sorting mechanism and population diversity maintenance strategy, can effectively select dominant individuals, making it particularly suitable for high-dimensional multi-objective optimization problems with at least three objective functions. This aligns perfectly with the model solution requirements of this paper, which involves eight objective functions. This application implements the NSGA-Ⅲ algorithm using the Plat Evolutionary Multi-objective Optimization (PlatEMO) platform in Matrix Laboratory Release 2020b (MATLAB R2020b). To ensure good convergence, the algorithm's operating parameters were finalized after multiple rounds of parameter tuning and trial calculations. The final operating parameters were set as follows: population size 480, maximum number of evaluations 100,000. Ultimately, the algorithm generated 450 Pareto optimal solutions, i.e., 450 sets of solutions, each containing 416 non-dominated decision variables.

[0105] In an exemplary embodiment, S4 specifically includes: S41: Construct the evaluation index matrix for each objective function corresponding to each scheme in the Pareto optimal solution set; one scheme corresponds to a set of Pareto optimal solutions.

[0106] S42: Using the entropy weight method, the objective weights of each objective function are calculated based on the data dispersion of the evaluation index matrix.

[0107] S43: Using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed based on expert experience, and a consistency check is performed to calculate the subjective weights of each objective function.

[0108] S44: Based on the objective weights and subjective weights, calculate the final combined weights of each objective function using a combined weighting formula.

[0109] S45: Inject the final combined weights into the TOPSIS model, normalize the evaluation index matrix, calculate the relative closeness of each scheme to the positive ideal solution and the negative ideal solution and arrange them in order, and determine the scheme with the highest relative closeness as the optimal configuration scheme.

[0110] In practical applications, this application employs the NSGA-III algorithm to solve a multi-objective optimization model for water conservation, pollution reduction, carbon reduction, greening, and growth, obtaining the Pareto optimal front that characterizes the trade-offs among multiple objectives. To select the most valuable optimization scheme for decision-making, addressing the limitation of single weighting methods in accurately depicting the synergistic relationships between complex objectives, a combined subjective and objective weighting model integrating the Analytic Hierarchy Process (AHP) and entropy weighting is constructed. This model is further combined with the TOPSIS method to comprehensively evaluate the Pareto solution set. Schemes are ranked based on their comprehensive scores, with the highest score used as the basis for subsequent analysis and discussion. This evaluation methodology effectively quantifies the interactions and trade-offs among multiple objectives, thereby selecting the comprehensive optimal configuration scheme that meets the sustainable development requirements of a specific economic zone, providing quantitative and scientific decision support for the green and low-carbon transformation of regional industrial structures.

[0111] In practical applications, the entropy weighting method is an objective weighting method based on the degree of data dispersion. Its core principle lies in using information entropy to measure the amount of information contained in the indicator data: the higher the dispersion of a certain indicator data (i.e., the lower the information entropy), the greater the amount of information that the indicator provides in distinguishing different evaluation schemes, and therefore it should be assigned a higher weight. For m evaluation schemes and n evaluation indicators, the evaluation index matrix is... The steps of the entropy weight method are as follows: (1) Normalization of indicator data: Positive indicators: (65) Negative indicators: (66) In the formula, for Standardized value and Representing the first j The maximum and minimum values ​​of each indicator. A positive indicator is one whose numerical change direction is consistent with the positive development trend of the evaluation target; that is, the larger the value, the better the evaluation result. A negative indicator is one whose numerical change direction is opposite to the expected trend of the evaluation target; that is, the smaller the value, the better the evaluation result.

[0112] (2) Determine the information entropy of the indicator: (67) (68) In the formula, Let be the entropy value of the j-th index.

[0113] (3) Determine the weights of the indicators: (69) In the formula, Let be the weight of the j-th indicator.

[0114] The following is a detailed explanation using specific symbols from this application: [The symbols here...] i Representing the i One option, i The value ranges from 1 to 450; j Representing the j Objective function j The value ranges from 1 to 8; specifically... j =1-8 correspond to water consumption, COD emissions, ammonia nitrogen emissions, economic benefits, carbon emissions, ecosystem service value, total phosphorus emissions, and total nitrogen emissions, respectively. x ij Then it represents the first i The first option j The original values ​​of each indicator. Positive indicators include economic benefits and ecosystem service value; the remaining objective functions are negative indicators. This section primarily focuses on standardizing the data from the 450 solutions obtained.

[0115] In practical applications, the Analytic Hierarchy Process (AHP) decomposes complex decision-making problems into hierarchical structures such as objectives, criteria, and solutions. Through expert experience, it compares and quantifies the elements at each level, and then calculates the relative weights of each element, providing a scientific basis that combines qualitative and quantitative analysis for the final decision.

[0116] The Analytic Hierarchy Process (AHP) is used to determine subjective weights. The AHP solution is based on expert review results and undergoes consistency checks using computer software. Once the check is passed, the expert scores are used as the final weights determined by the AHP. The formulas listed below are its mechanistic formulas.

[0117] The specific process is as follows: (1) Establish a hierarchical analysis structure model.

[0118] (2) Constructing the judgment matrix. In the criterion layer, each indicator is compared pairwise, and the numbers 1-9 and their reciprocals are used as standards to determine the importance of each indicator. The more important the indicator, the larger its value. Finally, the judgment matrix is ​​obtained. .

[0119] (3) Calculate the largest eigenvalue and the corresponding eigenvector of the judgment matrix and perform a consistency check.

[0120] ① Calculate the consistency index : (70) In the formula, is the largest eigenvalue of the judgment matrix; n is the order of the judgment matrix.

[0121] ② Calculate the consistency ratio : (71) In the formula, The matrix is ​​determined based on the randomness index value table to represent the average randomness index value. Average stochastic index value .

[0122] After the consistency check passes, the weights are normalized to obtain the final weight vector. , The resulting numerical values ​​represent the weights of each indicator.

[0123] In practical applications, the combined weighting method is a technique that integrates multiple individual weighting methods to comprehensively determine the weights of indicators. Substituting the weights determined by the two methods mentioned above into the combined weighting formula yields the final weights of the eight objective functions. This combined weighting method aims to combine the advantages of subjective and objective weighting methods to compensate for the arbitrariness that may exist in subjective judgment, while avoiding the excessive reliance on data structures in purely objective methods. This reduces information loss and makes the final weight allocation more reasonable, stable, and closer to the actual decision-making context. Based on this, this application adopts a subjective-objective combined weighting model that combines the analytic hierarchy process (AHP) and the entropy weighting method. The formula for calculating the combined weights is as follows: (72) In an exemplary embodiment, S45 specifically includes: S451: Normalize the positive and negative indicators in the evaluation indicator matrix to obtain a standardized matrix; wherein the positive indicators are the objective functions corresponding to economic benefits and ecosystem service value, and the negative indicators are the other objective functions.

[0124] S452: Based on the final combined weights, the standardized matrix is ​​weighted column by column to construct a weighted normalized matrix.

[0125] S453: Extract the maximum and minimum values ​​of each column in the weight normalization matrix to form the positive ideal solution vector and the negative ideal solution vector.

[0126] S454: Calculate the first Euclidean distance from each scheme in the normalized matrix to the positive ideal solution vector and the second Euclidean distance to the negative ideal solution vector.

[0127] S455: Calculate the comprehensive score of each scheme based on the first Euclidean distance and the second Euclidean distance; the closer the comprehensive score is to 1, the stronger the comprehensive performance of the scheme.

[0128] S456: The scheme with the highest overall score is selected as the optimal configuration scheme.

[0129] In practical applications, the TOPSIS method, also known as the "approximation of ideal solution ranking method," aims to rank and select the best solution from the set of solutions by measuring the relative proximity of each evaluated solution to the positive ideal solution (theoretically optimal solution) and the negative ideal solution (theoretically worst solution). This method has advantages such as simple calculation process, no strict restrictions on data structure, and intuitive and reasonable ranking results. Therefore, it is widely used in multi-attribute decision analysis and is applicable to the comprehensive evaluation of multi-objective optimization schemes in a certain economic zone, as described in this application.

[0130] This application determines the weights of eight objective functions and substitutes them into the TOPSIS formula to score and rank 450 solution sets, then selects the highest-scoring comprehensive optimal solution for subsequent result analysis.

[0131] The specific calculation steps of this method are as follows: (1) Calculate the weight normalization matrix Y: (73) (2) Find the maximum value of each column index in the objective function, denoted as . (i=1, 2, ..., m), forming a vector: (74) (3) Find the minimum value of each column index in the objective function, denoted as . (i=1, 2, ..., m) form a vector: (75) (4) Calculate the Euclidean distance. Each scheme corresponds to a set of objective functions. Define the distance from the i-th objective function set to the ideal objective as... : (76) (5) Define the distance from the i-th objective function set to the undesirable objective as The calculation formula is: (77) (6) Obtain evaluation and judgment directional data results T i : (78) T iThe range is between [0, 1]. When T i When the value is close to 1, it indicates that the overall evaluation value of the solution set is close to the ideal target, meaning that the overall performance of the solution is stronger. T i When the value is close to zero, the overall performance of the solution is weak.

[0132] (7) Sort the resource allocation schemes according to the performance scores to obtain the best overall resource allocation scheme.

[0133] Taking the actual application in 11 provinces and cities of a certain economic belt as an example, the technical solution of this application will be further explained.

[0134] 1. The Pareto solution set and TOPSIS comprehensive score obtained from the multi-objective optimization solution of a certain economic zone are as follows: Figure 3 As shown. The value ranges of each objective variable are: water consumption 194.84 billion - 197.027 billion m³. 3 The estimated emissions are as follows: COD 5.9818-6.199 million tons, NH3-N 100,200-102,100 tons, economic benefits 64.92-66.85 trillion yuan, CO2 emissions 3.274-3.646 billion tons, ecosystem service value 1.36-1.42 trillion yuan, TP emissions 78,100-80,000 tons, and TN emissions 554,500-565,000 tons. Based on the scheme with the highest TOPSIS score, it is selected as the final optimized result for pollution reduction, carbon reduction, and green growth in a certain economic zone, and a multi-dimensional analysis is conducted.

[0135] 2. Resource allocation results.

[0136] The optimization results of industrial output value of provinces and cities in a certain economic zone are as follows: Figure 4 As shown in the figure, after optimization, the total industrial output value of the region reached 63.39 trillion yuan, an increase of 1.75 trillion yuan compared to 2023, representing a growth rate of 2.84%. Looking at representative industries, the output value of the chemical products manufacturing industry (N11) was 8.45 trillion yuan, an increase of 0.7 trillion yuan; the output value of the metal smelting and rolling processing industry (N13) was 6.95 trillion yuan, an increase of 0.41 trillion yuan; the output value of the transportation equipment manufacturing industry (N17) was 6.09 trillion yuan, a decrease of 0.4 trillion yuan; the output value of the electrical machinery and equipment manufacturing industry (N18) was 5.78 trillion yuan, a decrease of 0.79 trillion yuan; and the output value of the computer, communication and other electronic equipment manufacturing industry (N19) was 6.74 trillion yuan, a decrease of 0.54 trillion yuan. The combined output value of these five major industries accounted for 53.65% of the total industrial output value, forming an important component of the region's industrial structure.

[0137] From the perspective of industry structure evolution, the output value of the electrical machinery and equipment manufacturing industry experienced a significant decline (down 12.02%), which is related to the suppression of its high energy consumption and high emission characteristics under the constraints of pollution reduction and carbon reduction. Meanwhile, the output value of industries such as metal smelting and rolling processing and chemical product manufacturing both achieved significant growth. In terms of inter-provincial distribution, Province E's industrial output value far surpasses others, reaching 17.17 trillion yuan, an increase of 0.08 trillion yuan compared to before optimization, representing a growth rate of 0.47%, accounting for 27.09% of the region's total industrial output value. Provinces B and F experienced larger fluctuations in industrial output value, with optimized output values ​​of 1.16 trillion yuan and 4.68 trillion yuan respectively, representing increases of 0.10 trillion yuan (9.43%) and 0.48 trillion yuan (11.44%) compared to before optimization, reflecting the optimization orientation of the central and western regions undertaking industrial transfer and accelerating industrialization. The industrial output value of City G and Province J changed relatively little, with growth rates of 3.38% (decline) and 0.31% (growth rate) respectively. Overall, the industrial output of all provinces and cities after optimization showed an upward trend to varying degrees, and the industry structure is evolving towards high-end and green development.

[0138] The optimization results of crop planting area in various provinces and cities of a certain economic zone are as follows: Figure 5As shown in the figure, after optimization, the total planting area of ​​crops in the region is 40.6223 million hectares, a decrease of 1.1167 million hectares compared to 2023, a decrease of 2.70%. In terms of crop structure, the planting area of ​​rice is 18.6514 million hectares, a decrease of 251,500 hectares, a decrease of 1.33%; the planting area of ​​wheat is 7.3426 million hectares, a decrease of 79,900 hectares, a decrease of 1.09%; the planting area of ​​corn is 7.7137 million hectares, a decrease of 30,600 hectares, a decrease of 0.40%; the planting area of ​​rapeseed is 5.8 million hectares, a decrease of 766,000 hectares, a decrease of 11.67%; and the planting area of ​​peanuts is 1.1146 million hectares, an increase of 511,200 hectares, a significant increase of 84.72%, the most significant increase among all crops. The substantial increase in peanut planting area is mainly due to its higher unit output value and obvious comparative advantage. Rice cultivation accounts for 45.91% of the total area, holding an absolute dominant position. This aligns perfectly with the traditional rice cultivation of southern China as a major grain crop and the region's abundant water resources. Wheat, corn, and rapeseed account for 18.07%, 19.00%, and 14.28% respectively, collectively forming the main structure of the region's agricultural sector. In terms of inter-provincial distribution, Province A has the largest crop cultivation area, reaching 7.2896 million hectares, accounting for 17.94% of the total area. Provinces D and F have the largest rice cultivation areas, at 3.8606 million hectares and 3.2731 million hectares respectively, consistent with their status as major rice-producing areas. Provinces A and E have relatively large wheat cultivation areas, at 2.8622 million hectares and 2.357 million hectares respectively, reflecting the planting advantages of the wheat system in the southern edge of the Huang-Huai-Hai Plain and the Jiang-Huai region. Provinces H and D have the largest rapeseed planting areas, at 1.2591 million hectares and 1.2715 million hectares respectively, which is closely related to the development and utilization of winter fallow fields and the tradition of oilseed crop planting in the two provinces.

[0139] The optimization results of livestock breeding volume in various provinces and cities of a certain economic zone are as follows: Figure 6As shown in the figure, after optimization, the total number of livestock raised in the region was 1.056 billion head, a decrease of 52 million head compared to 2023, a drop of 4.70%. In terms of the breeding structure, the average annual number of pigs raised was 155 million, a decrease of 40 million head, a drop of 20.51%; the number of beef cattle raised was 28.8914 million, a decrease of 3.6507 million head, a drop of 11.22%; the average annual number of poultry raised was 821 million birds, a decrease of 9 million birds, a drop of 1.08%; the number of dairy cattle raised was 1.4285 million, a decrease of 25,600 head, a drop of 1.76%; and the number of sheep raised was 49.3515 million, an increase of 328,000 head, an increase of 0.67%. Overall, under the optimized goals of water conservation, pollution reduction, carbon reduction, green expansion, and growth, except for a slight increase in sheep breeding, the scale of other livestock breeding showed a downward trend. The counter-trend growth in sheep farming is mainly related to a combination of factors, including lower carbon emission intensity per unit, lower pollutant emissions per unit, and higher output value per unit. Poultry farming accounts for 77.75% of the region's total annual livestock production, holding an absolute dominant position. This is closely related to the advantages of intensive farming, such as short breeding cycles, high feed conversion rates, less land occupation, and lower carbon emissions per unit output. Pigs, beef cattle, sheep, and dairy cattle account for 14.70%, 2.74%, 4.67%, and 0.14% respectively, collectively forming the main structure of the region's livestock industry.

[0140] The optimization results of aquaculture area in various provinces and cities of a certain economic zone are as follows: Figure 7 As shown in the figure, after optimization, the total area of ​​aquaculture in the region is 5190.96 kha, a decrease of 558.82 kha compared to 2023, a decrease of 9.72%. In terms of aquaculture type, pond aquaculture area is 1765.91 kha, a decrease of 6.15 kha, a decrease of 0.35%; lake aquaculture area is 466.58 kha, an increase of 21.04 kha, an increase of 4.72%; reservoir aquaculture area is 785.23 kha, a decrease of 32.4 kha, a decrease of 3.96%; river and ditch aquaculture area is 158.26 kha, a decrease of 7.25 kha, a decrease of 4.38%; and rice-fish aquaculture area is 2014.98 kha, a decrease of 534.07 kha, 20.95%. The decrease in rice-fish aquaculture area is the most significant, mainly because its CH4 emission intensity per unit area is higher and its ecological service value per unit area is weaker than that of ponds and reservoirs. Under the multi-objective collaborative optimization goal, area reduction became inevitable. The area of ​​lake aquaculture has increased against the trend, mainly due to the high output per unit and the relatively complete ecological service functions of lake aquaculture.

[0141] 3. Results of water consumption optimization.

[0142] Figure 8 This paper presents the optimized water consumption results for various provinces and cities within a certain economic zone. After optimization, the total regional water consumption is 191.819 billion m³. 3This represents a reduction of 8.657 billion m³ compared to before optimization. 3 The water-saving effect is significant. Province E has the highest water consumption, reaching 43.32 billion cubic meters. 3 This accounts for 22.58% of the region's total water consumption; Province C and Province A are next, with water consumption of 27.967 billion cubic meters respectively. 3 and 26.7 billion m 3 The proportions were 14.58% and 13.92% respectively. These three provinces combined contributed 51.08% of the region's total water consumption, making them the main water-consuming areas in this economic zone. In contrast, Province J and City G had lower water consumption, at 6.237 billion m³ respectively. 3 and 4.262 billion m 3 The proportions of water consumption for planting industries were 3.25% and 2.22%, respectively, which are closely related to their lighter industrial structure and higher water resource utilization efficiency. In terms of industry structure, the optimized water consumption for planting industries was 106.866 billion cubic meters. 3 Water consumption accounted for 54.63%, ranking first; industry followed, with a water consumption of 64.943 billion cubic meters. 3 Water consumption for fisheries and animal husbandry was 20.634 billion cubic meters, accounting for 33.20%; 3 and 3.182 billion m 3 The proportions were 10.55% and 1.62%, respectively. Livestock farming accounted for the smallest share, which is related to its relatively small scale of farming and low water intensity. Overall, the optimized water use structure further highlights the dominant position of crop farming, while the industrial and agricultural sectors still have significant water-saving potential.

[0143] 4. Results of pollutant emission optimization.

[0144] Figure 9This presentation showcases the optimized emissions of major pollutants (NH3-N, COD, TN, and TP) from various provinces and cities within a certain economic zone. After optimization, the total emissions of the four pollutants in the region were 101,300 tons, 6,027,100 tons, 557,400 tons, and 78,600 tons, respectively, representing reductions of 5,400 tons (5.06%), 679,400 tons (10.13%), 45,800 tons (7.59%), and 8,600 tons (9.86%) compared to before optimization. The significant pollution reduction effect reflects the strong emission reduction efficiency of the optimization scheme. In terms of inter-provincial distribution, Province D has the highest total pollutant emissions in the region, with NH3-N, COD, TN, and TP emissions of 18,400 tons, 1,106,100 tons, 93,500 tons, and 13,200 tons, respectively, accounting for 18.20%, 18.35%, 16.77%, and 16.76% of the total emissions of the same pollutants in the region. In contrast, the emissions from Province J, City K, and City G are relatively low. Specifically, Province J's emissions of the four pollutants are 0.60 million tons, 11.4 million tons, 1.97 million tons, and 0.33 million tons, respectively; City K's are 0.34 million tons, 21.39 million tons, 2.03 million tons, and 0.27 million tons, respectively; and City G's are 0.12 million tons, 3.16 million tons, 0.15 million tons, and 0.02 million tons, respectively. The lower emission levels in these provinces and cities are attributed to their lighter industrial structure, higher levels of pollution control, and relatively smaller proportion of agriculture.

[0145] 5. Optimization results of carbon emissions.

[0146] Figure 10 This presentation showcases the optimized carbon emission figures for various provinces and cities within a specific economic zone. After optimization, the region's total carbon emissions reached 3.507 billion tons, an increase of 0.95 billion tons, or 2.78%, compared to before optimization. In terms of inter-provincial distribution, Province E had the highest carbon emissions, reaching 815 million tons, accounting for 23.24% of the region's total emissions. This is attributed to its heavy industrial structure, high-energy-consuming industries, and large energy consumption. Provinces J and A followed, with 441 million tons and 388 million tons respectively, accounting for 12.57% and 11.06%. Cities G and K had relatively low carbon emissions, at 118 million tons and 108 million tons respectively, accounting for 3.36% and 3.08%, primarily due to their optimized industrial structures, low proportion of high-energy-consuming industries, and high levels of clean energy. In terms of industry structure, industry was the absolute dominant sector for regional carbon emissions, with optimized industrial carbon emissions reaching 3.222 billion tons, accounting for 91.88% of the region's total carbon emissions. Carbon emissions from crop farming, animal husbandry, and fisheries were 151 million tons, 112 million tons, and 22 million tons, respectively, accounting for 4.30%, 3.19%, and 0.63% of total emissions. Overall, the optimized carbon emission structure is characterized by industry dominance and significant inter-provincial differentiation, with eastern coastal provinces such as Province E and Province J being key areas for emission reduction.

[0147] 6. Results of optimization of ecosystem service value.

[0148] Figure 11 This study presents the optimized results of the ecosystem service value of various provinces and cities within a certain economic belt. After optimization, the total ecosystem service value of the region is 1,363.267 billion yuan, of which fishery ecosystem service value is 980.256 billion yuan, accounting for 71.90%; and crop farming ecosystem service value is 383.011 billion yuan, accounting for 28.10%. In terms of inter-provincial distribution, Province C has the highest fishery ecosystem service value, reaching 255.372 billion yuan, accounting for 26.05% of the total regional fishery ecosystem service value, which is closely related to its vast water area and abundant freshwater fishery resources. Provinces B, I, K, and G have relatively low fishery ecosystem service values, at 22.323 billion yuan, 23.796 billion yuan, 27.146 billion yuan, and 2.845 billion yuan respectively, accounting for less than 10% combined. Regarding crop farming ecosystem service value, Province A makes the most significant contribution, reaching 71.452 billion yuan, accounting for 18.66% of the total regional crop farming ecosystem service value, which is related to its extensive grain crop planting area. The ecological service value of crop farming in Province J, Province B, and City G is relatively low, at RMB 9.059 billion, RMB 15.822 billion, and RMB 1.212 billion respectively, accounting for less than 7% of the total value of the three provinces.

[0149] 7. Results of economic benefit optimization.

[0150] Results of economic efficiency optimization in various provinces and cities are as follows Figure 12 As shown in the figure. After optimization, the regional economic output reached 65.93 trillion yuan, an increase of 1.24 trillion yuan, or 1.92%, compared to before optimization. Province E had the highest economic efficiency, at 17.42 trillion yuan, accounting for 26.42% of the regional total; Province J was second, at 11.23 trillion yuan, accounting for 17.03%. Provinces B and I had relatively lower economic efficiency, at 1.32 trillion yuan and 2.31 trillion yuan respectively. The industrial sector exhibited significant characteristics of high output value and high carbon emissions. By province, Provinces E and J had the most outstanding industrial economic efficiency, at 17.17 trillion yuan and 11.16 trillion yuan respectively. While maintaining high economic efficiency, Province J's industrial sector had relatively low carbon emission intensity, demonstrating the positive results of green and low-carbon transformation. Province A had the highest economic efficiency in the planting industry, reaching 154.36 billion yuan. Provinces H and I lead in livestock economic benefits, with 199.077 billion yuan and 236.916 billion yuan respectively, reflecting their industrial advantages as major livestock-producing provinces. Province C has the highest fishery economic benefits, at 147.929 billion yuan, which is related to its abundant freshwater fishery resources and large-scale aquaculture. Overall, economic benefits and carbon emissions show a significant positive correlation; economically developed regions, especially provinces with high industrial efficiency, often have higher carbon emission levels.

[0151] It is evident that applying this multi-objective optimization technology to a certain economic zone has resulted in comprehensive benefits such as water conservation, pollution reduction, carbon reduction, greening, and growth due to changes in the industrial structure of industries such as industry, agriculture, fisheries, and animal husbandry.

[0152] Based on the practical application in 11 provinces and cities of a certain economic belt, the optimization technology developed in this application can achieve a synergistic improvement in economic and resource and environmental benefits. After the application of the technology, the industrial structure evolves towards greening, with the output value of high-energy-consuming industries such as electrical machinery and equipment manufacturing decreasing by 12.02%, while the overall industrial output value increases by 2.84%; the agricultural structure continues to optimize, with the planting area of ​​high-efficiency crops such as peanuts increasing by 84.72%, the total livestock output decreasing by 4.70%, and the aquaculture area decreasing by 9.72%; the synergistic effect is significant, with economic benefits increasing by 1.24 trillion yuan and water consumption reduced by 8.657 billion cubic meters. 3 The emission reduction of the four major pollutants ranged from 5.06% to 10.13%, the total carbon emissions increased by 2.78%, and the total value of ecosystem services reached 1.36 trillion yuan.

[0153] This application constructs a multi-dimensional, multi-objective optimization model for water conservation, pollution reduction, carbon reduction, and green growth. By integrating multi-dimensional data on economic benefits, water resource consumption, and environmental impacts (such as pollutant emissions and carbon emissions) across various industries within a region, it reveals multi-level optimization paths at the basin-province-industry level, providing a systematic solution for basin-wide sustainable development. This application expands the boundaries and dimensions of existing research in terms of spatial scale and objective dimensions. By optimizing the spatial layout and industrial structure of high water-consuming and high-emission industries within the basin, it addresses the dilemma of homogeneous industrial competition and pollution transfer, further promoting the coordinated implementation of the "dual-carbon" strategic goals and ecological protection red lines. Simultaneously, the differentiated policy recommendations generated by the model can provide a scientific basis for refined regulation measures such as basin-level water resource total quantity control and quota management, industry carbon emission total quantity control, agricultural non-point source pollution zone-based and classified management, and differentiated setting of ecological compensation standards, helping to shift regional governance from a "one-size-fits-all" approach to "categorized policy implementation." Ultimately, the research results will provide support for the coordinated advancement of basin environmental protection and green development, thereby achieving a win-win situation for both ecological and economic benefits.

[0154] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.

[0155] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0156] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0158] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0159] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0160] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A multi-objective, coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, characterized in that, include: The calculation model is used to calculate the water consumption, pollutant emissions, carbon dioxide emissions, economic benefits and ecological service value of various industries under multiple dimensions, and obtain the model parameters required for the optimization model. Based on the model parameters, the decision variables, objective functions, and constraints are determined, and a multi-objective optimization model is constructed to achieve water conservation, pollution reduction, carbon reduction, greening, and growth. The objective functions include minimizing water consumption, minimizing chemical oxygen demand emissions, minimizing ammonia nitrogen emissions, maximizing economic benefits, minimizing carbon dioxide emissions, maximizing ecosystem service value, minimizing total phosphorus emissions, and minimizing total nitrogen emissions. The objective function for minimizing water consumption is: ; min f 1 represents minimizing water consumption; M This is the sub-region index; m is the sub-region number; I Index for industrial sector types; i For industrial sector type serial numbers; J Crop type index; j For crop type serial number; Y Index for aquaculture types; y This refers to the serial number of the aquaculture type; K Index for livestock type; k For livestock type serial number; wIN mi Let i be the water consumption coefficient for industry i in subregion m; IN mi Let i be the industrial output value of industry i in subregion m; WC mj Let be the water use coefficient of crop j in subregion m; C mj Let be the planting area of ​​crop j in subregion m; WEF my The water consumption coefficient of type y in subregion m; EF my Let m be the aquaculture area of ​​type y in subregion m; S mk The proportion of livestock raised by farmers in subregion m is the proportion of livestock raised by farmers raising k animals. WS mk Let be the water consumption coefficient for livestock farmers with k animals in subregion m; FS mk Let m be the proportion of livestock raised by scale in subregion m; WFS mk Let be the water consumption coefficient for livestock farming of size k in subregion m; LS mk Let be the average annual number of livestock (k) in subregion m. The objective function that maximizes the value of ecosystem services is: ; maxf 6 To maximize the value of ecosystem services; GC mj Ecosystem service value coefficient of crop j in subregion m; GEF my represents the ecosystem service value coefficient of type y in subregion m; The objective function for minimizing total phosphorus emissions is: ; minf 7 To minimize total phosphorus emissions; PIN mi Let be the TP emission coefficient of industry i in subregion m; PC mj Let be the TP emission coefficient of crop j in subregion m; PS mk Let be the TP emission coefficient for livestock farmers with k livestock in subregion m; PFS mk Let be the TP emission coefficient for livestock farming of size k in subregion m; PEF my The emission coefficient of TP of type y in subregion m; The objective function for minimizing total nitrogen emissions is: ; minf 8 To minimize total nitrogen emissions; DIN mi Let be the TP emission coefficient of industry i in subregion m; DC mj Let be the TP emission coefficient of crop j in subregion m; DS mk Let be the TP emission coefficient for livestock farmers with k livestock in subregion m; DFS mk Let be the TP emission coefficient for livestock farming of size k in subregion m; DEF my The emission coefficient of TP of type y in subregion m; A third-generation non-dominated sorting genetic algorithm is used to iteratively calculate a multi-objective optimization model containing the aforementioned decision variables, objective function, and constraints to generate a Pareto optimal solution set. Based on the Pareto optimal solution set, the TOPSIS method based on combined weighting is used for comprehensive evaluation and ranking, and the optimal configuration scheme with the highest comprehensive score is output; the optimal configuration scheme is a water-saving, pollution-reducing, carbon-reducing, greening and growth synergistic optimization scheme.

2. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 1, is characterized in that... The calculation model is used to calculate water consumption, pollutant emissions, carbon dioxide emissions, economic benefits, and ecosystem service value for various industries across multiple dimensions, thereby obtaining the model parameters required for optimization. These parameters include: Industrial water consumption is calculated based on industrial output value and industrial water intensity, and agricultural water consumption is calculated based on planting area, average annual livestock breeding volume and aquaculture area and corresponding water consumption coefficients, so as to obtain the water consumption coefficients of each industry in each sub-region. Carbon emission coefficients were used to calculate carbon dioxide emissions from industry, crop farming, animal husbandry, and fisheries respectively. Among them, the carbon emission calculation for crop farming and fisheries included direct carbon emissions from material inputs and energy consumption, indirect carbon emissions of greenhouse gases, and carbon absorption, to obtain carbon dioxide emission coefficients for each industry in each sub-region. The total emissions of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen are calculated based on the unit emission coefficients of each industry, and the pollutant emission coefficients of each industry in each sub-region are obtained. Economic benefits are calculated based on historical output data, and ecological service value is calculated based on standard ecological service value equivalent factor, resulting in economic benefit coefficients and ecological service value coefficients for each industry in each sub-region. The water consumption coefficient, carbon dioxide emission coefficient, pollutant emission coefficient, economic benefit coefficient, and ecosystem service value coefficient of each industry in each sub-region are substituted into the multi-objective optimization model as model parameters, and the total amount of each indicator in the baseline year is used as the upper or lower limit of the constraints.

3. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 1, is characterized in that... The decision variables include the industrial output of each industrial sector in the sub-region, the planting area of ​​various crops, the average annual breeding volume of various livestock, and the aquaculture area of ​​various types of fisheries. The objective functions are constructed as linear combinations of decision variables and corresponding model parameters, respectively minimizing water consumption, minimizing chemical oxygen demand emissions, minimizing ammonia nitrogen emissions, maximizing economic benefits, minimizing carbon dioxide emissions, maximizing ecosystem service value, minimizing total phosphorus emissions, and minimizing total nitrogen emissions. Each objective function participates in fitness assessment simultaneously during the iterative calculation process.

4. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 1, is characterized in that... The constraints include economic development constraints, restrictions on scale expansion, restrictions on pollutant emissions, restrictions on livestock development planning, restrictions on fishery development planning, restrictions on agricultural development planning, and constraints on the availability of water resources. The economic development constraint is configured such that the optimized economic benefits are greater than the unoptimized economic benefits. The scale expansion constraint is set based on the largest expansion rate in recent years and the smallest scale in the past five years to set the upper and lower limits of the decision variables. The pollutant emission limit constraint is configured such that the optimized pollutant emission reduction rate is greater than the minimum reduction rate required by the policy plan; The water resource availability constraint is configured such that the optimized total water consumption is lower than the upper limit of the total available water resources in the region. The multi-objective optimization model performs optimization calculations within the feasible region defined by the constraints.

5. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 1, is characterized in that... A third-generation non-dominated sorting genetic algorithm is used to iteratively calculate a multi-objective optimization model containing the aforementioned decision variables, objective function, and constraints to generate a Pareto optimal solution set, specifically including: Initialize the population by encoding the decision variables as chromosome individuals and setting the population size and maximum number of evaluations; The third-generation non-dominated sorting genetic algorithm is adopted to perform non-dominated sorting mechanism to stratify individuals in the population, and the crowding degree of individuals is calculated by combining the reference point strategy. Based on the stratification results and individual crowding, selection, crossover, and mutation operations are performed to generate offspring populations. Parents and offspring are then merged for environmental selection. This process is repeated iteratively until the maximum number of evaluations is reached, outputting a Pareto optimal solution set containing multiple sets of non-dominated decision variables. Each Pareto optimal solution satisfies the constraints and makes the objective function achieve a multidimensional trade-off optimality.

6. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 1, is characterized in that... Based on the Pareto optimal solution set, the TOPSIS method based on combinatorial weighting is used for comprehensive evaluation and ranking, outputting the optimal configuration scheme with the highest comprehensive score, specifically including: Construct an evaluation index matrix for each objective function corresponding to each scheme in the Pareto optimal solution set; one scheme corresponds to a set of Pareto optimal solutions; The objective weights of each objective function are calculated using the entropy weight method, based on the data dispersion of the evaluation index matrix. The Analytic Hierarchy Process (AHP) is used to construct a judgment matrix based on expert experience and perform consistency checks to calculate the subjective weights of each objective function. Based on the objective weights and subjective weights, the final combined weights of each objective function are calculated using a combined weighting formula; The final combined weights are injected into the TOPSIS model, the evaluation index matrix is ​​normalized, the relative closeness of each scheme to the positive ideal solution and the negative ideal solution is calculated and arranged in order, and the scheme with the highest relative closeness is determined as the optimal configuration scheme.

7. The multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in claim 6, is characterized in that... The final combined weights are injected into the TOPSIS model, the evaluation index matrix is ​​normalized, the relative closeness of each scheme to the positive and negative ideal solutions is calculated and arranged in order, and the scheme with the highest relative closeness is determined as the optimal configuration scheme, specifically including: The positive and negative indicators in the evaluation index matrix are normalized to obtain a standardized matrix; wherein the positive indicators are the objective functions corresponding to economic benefits and ecosystem service value, and the negative indicators are the other objective functions. Based on the final combined weights, the standardized matrix is ​​weighted column by column to construct a weighted normalized matrix; The maximum and minimum values ​​of each column in the weight normalization matrix are extracted to form the positive ideal solution vector and the negative ideal solution vector; Calculate the first Euclidean distance from each scheme in the normalized matrix to the positive ideal solution vector and the second Euclidean distance to the negative ideal solution vector; The comprehensive score of each scheme is calculated based on the first Euclidean distance and the second Euclidean distance; the closer the comprehensive score is to 1, the stronger the comprehensive performance of the scheme. The solution with the highest overall score is selected as the optimal configuration.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-objective coordinated optimization method based on water conservation, pollution reduction, carbon reduction, greening, and growth, as described in any one of claims 1-6.

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